 |
Before our proposal, I would like to give a brief review of existing methods. In traditional detection methods, researchers developed the variant of the Shiryaev-Roberts's procedure, Checkpoint Anomaly Detection techniques, and multifractal detrended fluctuation analysis (MF-DFA) and so on. Researchers also used ML and AI into LDDoS, such as BIRTH algorithm, wavelet transform, propagation back (BP) model of neural network, ANN and the optimizer-based ANN technology, convolution neural network, SVM, LPA etc. Although these methods have high accuracy, they need more training and detecting time when new samples were labeled. Thus, we propose and develop a novel semi-supervised locality sensitive incremental TSVM method for higher detecting accuracy and fast training and response time. |
IEEE/ICACT20210143 Slide.05
[Big Slide]